As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Hybrid Mechanism of Deep Q-Network (DQN) and Graph Mining Algorithm in “Six-Dimensional Integrated” Mental Health Assessment and Prediction: Taking Hefei University of Economics as an Example
How to evaluate the mental health status of college students? How to establish evaluation indicators for the mental health status of college students? How to predict mental health status? These are important issues faced by mental health education in universities. In response to the above issues, the article proposed a hybrid mechanism based on Deep Q-Network (DQN) and graph mining algorithm, and applied it to the “Six-dimensional Integrated” mental health assessment and prediction at Hefei University of Economics. The experimental results show that the prediction method based on Deep Q-Network has the highest accuracy of 98%, the highest recall rate of 88%, and the highest stability of 96%. From this, it can be seen that the proposed mixed mechanism of DQN and graph mining can provide effective evaluation and prediction for the mental health status of college students.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.